本文關(guān)鍵詞： 復(fù)雜網(wǎng)絡(luò) 非線性 互信息 無標(biāo)度特性 波動聚集 HHT方法 收益和波動 溢出效應(yīng)　出處：《南京信息工程大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：金融市場是一個大尺度的動力學(xué)復(fù)雜系統(tǒng),資產(chǎn)價格的波動性一直是金融動力學(xué)研究的一個重要課題,傳統(tǒng)的研究方法是通過建立多元統(tǒng)計的模型對有限股票之間的波動關(guān)聯(lián)性進行研究,然而,市場上每兩只股票之間都互相關(guān)聯(lián),基于此,本文引入復(fù)雜網(wǎng)絡(luò)理論通過分析股票網(wǎng)絡(luò)來全面揭示股票之間的波動關(guān)聯(lián)性。波動溢出效應(yīng)也是證券市場波動性研究當(dāng)中的一個重要方向,本文引入經(jīng)驗?zāi)B(tài)分解的方法通過滬深股市波動的相位關(guān)系動態(tài)分析滬深市場之間的溢出效應(yīng)。本文的主要工作和創(chuàng)新成果如下： (1)以上海市場2001年1月2日至2010年12月7日中的501只股票作為研究樣本,根據(jù)股票價格波動非線性關(guān)聯(lián)的長度確定了觀測窗口的時間長度,然后采用互信息法和滑動窗口的方法構(gòu)建了2000個動態(tài)股票關(guān)聯(lián)網(wǎng)絡(luò)。 (2)分析了2000個動態(tài)股票關(guān)聯(lián)網(wǎng)絡(luò)的平均度、平均簇系數(shù)、冪指數(shù)、擬合誤差和擬合優(yōu)度p值隨時間的變化情況,研究結(jié)果發(fā)現(xiàn)2005年7月1日,2007年10月16日和2008年12月1日左右的這三段時期的股票關(guān)聯(lián)網(wǎng)絡(luò)不具有無標(biāo)度特性,且這三段時期是上海市場的轉(zhuǎn)折時期。 (3)分析了大盤指數(shù)波動聚集程度與股票網(wǎng)絡(luò)拓撲結(jié)構(gòu)變化之間的關(guān)系,實證發(fā)現(xiàn)在證券市場的轉(zhuǎn)折時期股票網(wǎng)絡(luò)不具備無標(biāo)度特性,各股票價格波動之間的相互依賴性變?nèi)?大盤指數(shù)呈隨機波動行為；相反,在證券市場正常發(fā)展時期股票網(wǎng)絡(luò)具有無標(biāo)度特性,大盤指數(shù)的波動聚集度較高。所以在一定意義上股票網(wǎng)絡(luò)的無標(biāo)度特性是股票市場正常發(fā)展的標(biāo)志。 (4)以上證指數(shù)和深證綜合指數(shù)1991年12月30日至2007年10月8日的收盤價為研究樣本,利用EMD方法獲取了滬深兩個市場日收盤價的收益和波動的趨勢成分,然后通過希爾伯特變換獲得各成分的瞬時相位和瞬時幅度。根據(jù)滬深兩市收益與波動趨勢成分相位關(guān)系的時變特征,揭示了滬深股市的動態(tài)溢出效應(yīng),且發(fā)現(xiàn)2001年是中國股市的轉(zhuǎn)折之年。 本文的分析方法和研究結(jié)論將有助于我國證券市場上的投資者做出正確的投資選擇,減少損失,保持我國國民經(jīng)濟長期穩(wěn)定健康的發(fā)展。
[Abstract]:Financial market is a large-scale dynamic complex system, the volatility of asset prices has been an important subject in the research of financial dynamics. The traditional research method is to study the volatility correlation between limited stocks by establishing a multivariate statistical model. However, every two stocks in the market are interrelated, based on this, In this paper, the complex network theory is introduced to comprehensively reveal the volatility correlation between stocks by analyzing the stock network. Volatility spillover effect is also an important direction in the research of volatility in the securities market. In this paper, the empirical mode decomposition method is introduced to dynamically analyze the spillover effects between Shanghai and Shenzhen stock markets through the phase relationship of the volatility in Shanghai and Shenzhen stock markets. The main work and innovative results of this paper are as follows:. Using 501 stocks in Shanghai market from January 2nd 2001 to December 7th 2010 as the research sample, the time length of the observation window is determined according to the length of nonlinear correlation of stock price fluctuations. Then 2000 dynamic stock correlation networks are constructed by mutual information method and sliding window method. (2) the variation of average degree, average cluster coefficient, power exponent, fitting error and goodness of fit p over time of 2000 dynamic stock correlation networks are analyzed. The results show that the stock correlation networks of July 1st 2005, October 16th 2007 and December 1st 2008 do not have scale-free characteristics, and these three periods are the turning points of the Shanghai market. This paper analyzes the relationship between the aggregation degree of large market index volatility and the change of the topological structure of stock network. It is found that the stock network does not have scale-free characteristics in the transition period of the stock market, and the interdependence among the stock price fluctuations becomes weaker. On the contrary, in the normal development of the stock market, the stock network has scale-free characteristics. Therefore, the scale-free characteristic of the stock network is the symbol of the normal development of the stock market in a certain sense. Taking the closing price of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Composite Index from December 30th 1991 to October 8th 2007 as the research sample, using EMD method to obtain the earnings and trend components of the daily closing price of Shanghai and Shenzhen markets. Then the instantaneous phase and instantaneous amplitude of each component are obtained by Hilbert transform. According to the time-varying characteristics of the relationship between returns and the phase of fluctuating trend components, the dynamic spillover effect of Shanghai and Shenzhen stock markets is revealed. And find 2001 is the turning point year of Chinese stock market. The analytical methods and conclusions of this paper will be helpful for investors in China's securities market to make correct investment choices, reduce losses, and maintain the long-term, stable and healthy development of China's national economy.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F832.51

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本文編號：1508475